Support vector tracking

Author

Avidan, Shai

Author_Institution

MobilEye Vision Technol. LTD, Jerusalem, Israel

Volume

26

Issue

8

fYear

2004

Firstpage

1064

Lastpage

1072

Abstract

Support Vector Tracking (SVT) integrates the Support Vector Machine (SVM) classifier into an optic-flow-based tracker. Instead of minimizing an intensity difference function between successive frames, SVT maximizes the SVM classification score. To account for large motions between successive frames, we build pyramids from the support vectors and use a coarse-to-fine approach in the classification stage. We show results of using SVT for vehicle tracking in image sequences.

Keywords

image classification; image sequences; minimisation; support vector machines; vehicles; SVM classification; coarse to fine approach; image sequences; intensity difference function; maximization; minimization; optic flow based tracker; successive frames; support vector machine classifier; support vector tracking; vehicle tracking; Brightness; Cameras; Image sequences; Integrated optics; Optical filters; Optical sensors; Support vector machine classification; Support vector machines; Vehicle detection; Video sequences; Support vector machines; optic-flow; visual tracking.; Algorithms; Artificial Intelligence; Computer Graphics; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Information Storage and Retrieval; Movement; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Subtraction Technique; Video Recording;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/TPAMI.2004.53

Filename

1307012